IP Library Patent Application 17734989
Patent Application
App. No. 17/734,989

Systems and Methods for Generating and Using Anthropomorphic Signatures to Authenticate Users

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Quick Facts
Patent No.
US None
App. No.
17/734,989
Abstract

The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.

Claims (37)

1 . A computer-implemented method of authentication using a plurality of non-deterministic authentication biometric inputs, the method including:

receiving a plurality of non-deterministic biometric inputs with a request for authentication;

feeding the non-deterministic biometric inputs to a trained machine learning model and generating a set of authentication feature vectors, wherein the non-deterministic authentication biometric input includes an image and a voice sample of a user;

projecting the set of feature vectors onto a surface of a hyper-sphere; and

authenticating the user when a cosine distance between the authentication feature vector and a characteristic identity vector previously registered for the user is less than a pre-determined threshold.

2 . The method of claim 1 , wherein the sets of feature vectors are projected onto a surface of a unit hyper-sphere.

3 . The method of claim 1 , wherein the characteristic identity vector for the user was determined by averaging feature vectors for from a plurality of images and for a plurality of voice samples on a user-by-user basis.

4 . The method of claim 3 , wherein the predetermined threshold was determined from variance among projected feature vectors from the plurality of images and the plurality of voice samples on a user-by-user basis.

5 . The method of claim 3 , wherein the predetermined threshold was determined from variance among projected feature vectors from the plurality of images and the plurality of voice samples for classes of users.

6 . The method of claim 3 , wherein the predetermined threshold was determined from variance among projected feature vectors from the plurality of images and the plurality of voice samples across users.

7 . A non-transitory computer readable storage medium impressed with computer program instructions to authenticate using a plurality of non-deterministic authentication biometric inputs, the instructions, when executed on a processor, implement a method comprising:

receiving a plurality of non-deterministic biometric inputs with a request for authentication;

feeding the non-deterministic biometric inputs to a trained machine learning model and generating a set of authentication feature vectors, wherein the non-deterministic authentication biometric input includes an image and a voice sample of a user;

projecting the set of feature vectors onto a surface of a hyper-sphere; and

authenticating the user when a cosine distance between the authentication feature vector and a characteristic identity vector previously registered for the user is less than a pre-determined threshold.

8 . The non-transitory computer readable storage medium of claim 7 , wherein the sets of feature vectors are projected onto a surface of a unit hyper-sphere.

9 . The non-transitory computer readable storage medium of claim 7 , wherein the characteristic identity vector for the user was determined by averaging feature vectors for from a plurality of images and for a plurality of voice samples on a user-by-user basis.

10 . The non-transitory computer readable storage medium of claim 9 , wherein the predetermined threshold was determined from variance among projected feature vectors from the plurality of images and the plurality of voice samples on a user-by-user basis.

11 . The non-transitory computer readable storage medium of claim 9 , wherein the predetermined threshold was determined from variance among projected feature vectors from the plurality of images and the plurality of voice samples for classes of users.

12 . The non-transitory computer readable storage medium of claim 9 , wherein the predetermined threshold was determined from variance among projected feature vectors from the plurality of images and the plurality of voice samples across users.

13 . A system including one or more processors coupled to memory, the memory loaded with computer instructions to authenticate using a plurality of non-deterministic authentication biometric inputs, when executed on the processors implement the instructions of claim 7 .

14 . The system of claim 13 , wherein the sets of feature vectors are projected onto a surface of a unit hyper-sphere.

15 . The system of claim 13 , wherein the characteristic identity vector for the user was determined by averaging feature vectors for from a plurality of images and for a plurality of voice samples on a user-by-user basis.

16 . The system of claim 15 , wherein the predetermined threshold was determined from variance among projected feature vectors from the plurality of images and the plurality of voice samples on a user-by-user basis.

17 . The system of claim 15 , wherein the predetermined threshold was determined from variance among projected feature vectors from the plurality of images and the plurality of voice samples for classes of users.

18 . The system of claim 15 , wherein the predetermined threshold was determined from variance among projected feature vectors from the plurality of images and the plurality of voice samples across users.

19 . A computer-implemented method of authentication using a plurality of non-deterministic authentication biometric inputs, the method including:

receiving the plurality of non-deterministic authentication biometric inputs with a request for authentication;

feeding the non-deterministic authentication biometric inputs to a plurality of trained machine learning models and generating a plurality of authentication feature vectors wherein the non-deterministic authentication biometric input includes an image and a voice sample of a user;

applying a distance preserving hash to the plurality of authentication feature vectors to generate an authentication hash representing the user; and

authenticating the user when a difference between the authentication hash and a registration hash previously registered for the user is less than a pre-determined threshold.

20 . A computer-implemented method of authentication using a plurality of non-deterministic authentication biometric inputs, the method including:

receiving the plurality of non-deterministic authentication biometric inputs with a request for authentication;

feeding the non-deterministic authentication biometric inputs to a plurality of trained machine learning models and generating a plurality of authentication feature vectors wherein the non-deterministic authentication biometric input includes an image and a voice sample of a user;

applying a binning function to non-integer values in the plurality of authentication feature vectors to quantize the non-integer values to integer values;

applying a hash function to the plurality of quantized authentication feature vectors to generate an authentication hash; and

authenticating the user when the authentication hash matches a registration hash previously registered for the user.

Assignments (3)
SECURITY INTEREST Recorded Oct 22, 2024
From: HEALTHWAYS SC, LLC; SHARECARE AI, INC.
To: WELLS FARGO BANK, NATIONAL ASSOCIATION
Reel/Frame 068977/0210 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 2, 2022
From: SLY, AXEL; SHARMA, SRIVATSA AKSHAY; REDINGER, BRETT ROBERT; REICH, DEVIN DANIEL; TROOSKENS, GEERT; LOOTUS, MEELIS; LEE, YOUNG JIN; ARREDONDO, RICARDO LOPEZ; KAUTZ, FREDERICK FRANKLIN, IV; BHAT, SATISH SRINIVASAN; KIRK, SCOTT MICHAEL; DE BROUWER, WALTER ADOLF; THAKORE, KARTIK
To: DOC.AI, INC.
Reel/Frame 059786/0056 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 2, 2022
From: DOC.AI, INC.
To: SHARECARE AI, INC.
Reel/Frame 059786/0101 →